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| #pip install rank-bm25 | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from sklearn.model_selection import train_test_split | |
| from rank_bm25 import BM25Okapi | |
| # Read CSV file | |
| data = pd.read_csv('books.csv', encoding='latin1') | |
| class TFIDFDoc2Vec: | |
| def __init__(self): | |
| self.tfidf_vectorizer = TfidfVectorizer() | |
| self.doc_vectors = None | |
| def initialize_vectors(self, documents): | |
| tfidf_matrix = self.tfidf_vectorizer.fit_transform(documents) | |
| self.doc_vectors = tfidf_matrix.toarray() | |
| def find_similar_documents(self, query, top_n=10): | |
| query_vector = self.tfidf_vectorizer.transform([query]).toarray() | |
| similarities = cosine_similarity(query_vector, self.doc_vectors) | |
| similar_indices = similarities.argsort(axis=1)[:, ::-1][:, :top_n] | |
| similar_documents = [] | |
| for indices in similar_indices: | |
| similar_documents.append(indices) | |
| return similar_documents | |
| def rank_bm25(self, query, bm25_model, documents, top_n=10): | |
| scores = bm25_model.get_scores(query) | |
| top_indices = np.argsort(scores)[::-1][:top_n] | |
| return top_indices | |
| data | |
| # Select the column containing book titles | |
| documents = data['Book Title'].astype(str) | |
| # Initialize TF-IDF vectors and model | |
| tfidf_doc2vec_model = TFIDFDoc2Vec() | |
| tfidf_doc2vec_model.initialize_vectors(documents) | |
| # Initialize BM25 model | |
| bm25_model = BM25Okapi(documents.str.split()) | |
| def answer(query): | |
| # Find similar documents | |
| similar_documents_indices = tfidf_doc2vec_model.find_similar_documents(query) | |
| # Rank similar documents using BM25 | |
| similar_documents_indices_bm25 = tfidf_doc2vec_model.rank_bm25(query, bm25_model, documents) | |
| # Initialize a list to store ranked documents | |
| ranked_documents = [] | |
| # Add details of each document to the list | |
| for indices in similar_documents_indices: | |
| for index in indices: | |
| document_details = { | |
| "Book": data['Book Title'].iloc[index], | |
| "Author": data['Author'].iloc[index], | |
| "Edition": data['Edition'].iloc[index], | |
| "File Name": data['File_name'].iloc[index] | |
| } | |
| ranked_documents.append(document_details) | |
| return ranked_documents | |
| # Receive query from the user | |
| #query = input("Enter your query: ") | |
| #result = answer(query) | |
| #print(result) |